Obstructive sleep apnea syndrome (OSAS) is the most common form of sleep disordered-breathing. Epidemiological studies estimate its prevalence up to 5% of adult men in western countries. OSAS is characterized by repetitive occlusion of the upper airway during sleep, causing intermittent cessations of breathing (apneas) or reduction in airflow (hypopneas). Events of apnea are accompanied by hypoxemia and bradycardia. They are often terminated in arousals, and the resulting sleep fragmentation can lead to excessive daytime sleepiness. As a result, OSAS has been pointed out as a major cause of traffic and industrial accidents. Additionally, long-term effects are related to the cardiovascular system, including hypertension, arrhythmias, congestive heart failure and cerebrovascular disease. A high percentage of patients, 83% of men and 93% of women, remains undiagnosed. Therefore, OSAS can be considered as a risk factor for public health.
Nowadays, nocturnal polysomnography (PSG) is considered the gold-standard for OSAS diagnosis. It must be performed in a special sleep unit and under supervision of a trained technician. PSG monitors different physiological recordings such as electrocardiogram (ECG), electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), oxygen saturation, abdominal ventilatory effort and snoring. These recordings must be subsequently analyzed by a medical expert to obtain a final diagnosis. Despite its high diagnostic performance, PSG presents some drawbacks since it is complex, expensive and time-consuming. As a result, the research focused on the development of alternative diagnostic techniques has notably increased in recent years, such us the use of medical systems based on nocturnal pulse oximetry.
Nocturnal pulse oximetry allows to monitor respiratory dynamics during sleep by measuring arterial oxygen saturation (SaO2). This recording provides useful information about OSAS. Events of apnea are characterized by a decrease in the SaO2 value, which reflects airflow reduction and hypoxemia. Subsequently, respiration is restored and the saturation value increases until its baseline level. As a result, SaO2 signals from OSAS patients tend to be more unstable than those from control subjects due to the recurrence of apneas during sleep. This different behavior can be exploited to diagnose OSAS. Diverse methodologies have been proposed to perform OSAS diagnosis from SaO2 data. The simplest one is visual inspection. However, it is tedious and subjective. Therefore, automated analysis of SaO2 data would be desirable. Conventional oximetry indices represent a first approach for this purpose. These indices are the oxygen desaturation index over 2% (ODI2), 3% (ODI3) and 4% (ODI4), and the cumulative time spent below 90% of saturation (CT90). However, improved OSAS diagnosis from SaO2 recordings is possible by using more advanced computer-implemented signal processing methods.
Related art includes U.S. patent application Ser. No. 10/947,983 which discloses a method for diagnosing OSAS based on a tool for the predicting Apnea Hypopnea Index (AHI) using non-parametric analysis and bootstrap aggregation; U.S. patent application Ser. No. 11/122,278 which discloses a method for monitoring respiration involving processing plethysmography signals; and U.S. patent application Ser. No. 10/30/2008 which discloses a computer-implemented method for patient monitoring based on processing signals to detect breathing patterns. Disadvantageously, none of the related art provides a complete method or system that enables for automatic detection of OSAS based on pulse oximetry with classification accuracies appropriate for clinical use.